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Predicting customer churn in telecommunication industry using convolutional neural network model
In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in past researches. However, in the building of these models, the...
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| Format: | Article |
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2020-06
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/11370 | ||
| 042 | |a dc | ||
| 720 | |a Amatare, S. A. |e author | ||
| 720 | |a Ojo, A. K. |e author | ||
| 260 | |c 2020-06 | ||
| 520 | |a In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in past researches. However, in the building of these models, there is need for human intervention to carry out attribute selection which is very tedious, time-consuming, tailored to specific datasets and often result to attribute selection problems. This study proposed a convolutional neural network model for predicting customer churning behavior and to also get rid of human attribute selection and its problems. Two datasets were created from the fourteen thousand data instances that were gotten from one of the major cellular companies operating in Nigeria. Python programming language via the anaconda distribution was used for the development and implementation of our model. Jupyter notebook was our IDE choice. In other to achieve a like-for-like comparison, three other models were developed, which were two Multi-layer Perceptron (MLP) models and one other CNN model. The accuracy rates for the MLP models; MLP1 and MLP2, are 80% and 81% respectively while the CNN models, CNN1 and CNN2, are 81% and 89% respectively. | ||
| 024 | 8 | |a 2278-0661 | |
| 024 | 8 | |a ui_art_ojo_predicting_2020 | |
| 024 | 8 | |a IOSR Journal of Computer Engineering 22(3), pp. 54-59 | |
| 024 | 8 | |a https://repository.ui.edu.ng/handle/123456789/11370 | |
| 653 | |a Convolutional Neural Network | ||
| 653 | |a Customer Churn | ||
| 653 | |a Attribute selection | ||
| 653 | |a Multi-layer Perceptron | ||
| 245 | 0 | 0 | |a Predicting customer churn in telecommunication industry using convolutional neural network model |